Lab2

Author

Harshini Karthikeyan

Published

Invalid Date

Lab 2

Show code

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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library(dplyr)
library(ggplot2)
library(readxl)
library(ggrepel)
library(scales)


Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

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library(leaflet)

library(rnaturalearth)
library(sf)

Linking to GEOS 3.13.0, GDAL 3.10.1, PROJ 9.5.1; sf_use_s2() is TRUE

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library(rnaturalearthdata)


Attaching package: 'rnaturalearthdata'

The following object is masked from 'package:rnaturalearth':

    countries110

Part 1:

While there are certainly issues with this image, do your best to tell the story of this graph in words. That is, what is this graph telling you? What do you think the authors meant to convey with it?

I believe this plot is trying to show us the proportion of people in a particular country that believe a vaccine is safe. It shows us one observation per country where the y-axis appears to be country? It also sorted the observations by proportion, so that it appears that the proportions increase as the y-axis goes up.

List the variables that appear to be displayed in this visualization. Hint: Variables refer to columns in the data.

The country, the global region of the country and the percentage of people in the country that believe a vaccine is safe.

Now that you’re versed in the grammar of graphics (e.g., ggplot), list the aesthetics used and which variables are mapped to each.

The x-axis the percentage of people in the country that believe a vaccine is safe. The y-axis is the country, the color is filled by global region.

What type of graph would you call this? Meaning, what geom would you use to produce this plot?

I would call this a scatter plot and would use geom point to produce it.

Provide at least four problems or changes that would improve this graph. Please format your changes as bullet points!

I think this is a graph that is faceted by region of the world, but it lends to a strange stacked graph like view that is not very intuitive, and leaves some plots stranded from their axes. I would try faceting differently or trying to remove the facet entirely.
I think having the y-axis be the countries is a bit strange, and that sorting it by the proportion feels odd, as though it is increasing over time or something, I would try sorting it alphabetically or removing the y-axis and making it a box-plot instead.
I think having the legend at the bottom is unnecessary as the global region is also written directly onto each facet of the plot, so I would remove one of those, preferably the legend, depending on how my plot turns out.
I think having to state that the dark vertical lines represent region medians but also not writing the number of each on the plot itself is confusing and forces the reader to then look for th median by finding the axis

Improving the Bad Visualization

Improve the visualization above by either re-creating it with the issues you identified fixed OR by creating a new visualization that you believe tells the same story better.

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data<-read_excel("C:\\Users\\Geetha\\Downloads\\wgm2018-dataset-crosstabs-all-countries.xlsx")

New names:
• `` -> `...1`
• `` -> `...2`
• `` -> `...3`
• `` -> `...5`
• `` -> `...7`
• `` -> `...8`
• `` -> `...9`
• `` -> `...11`
• `` -> `...12`
• `` -> `...13`
• `` -> `...14`
• `` -> `...15`
• `` -> `...17`
• `` -> `...18`
• `` -> `...19`
• `` -> `...20`
• `` -> `...21`
• `` -> `...23`
• `` -> `...24`
• `` -> `...25`
• `` -> `...27`
• `` -> `...28`
• `` -> `...29`
• `` -> `...31`
• `` -> `...32`
• `` -> `...33`
• `` -> `...34`
• `` -> `...35`
• `` -> `...36`
• `` -> `...37`
• `` -> `...38`
• `` -> `...39`
• `` -> `...41`
• `` -> `...42`
• `` -> `...43`
• `` -> `...44`
• `` -> `...45`

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head(data)

# A tibble: 6 × 45
  ...1        ...2       ...3  `National results` ...5  Gender ...7  ...8  ...9 
  <chr>       <chr>      <chr> <chr>              <chr> <chr>  <chr> <chr> <chr>
1 <NA>        <NA>       <NA>  Country total      <NA>  Male   <NA>  Fema… <NA> 
2 Country     Question   Resp… Column N %         Unwe… Colum… Unwe… Colu… Unwe…
3 Afghanistan Q1 How mu… A lot 2.456111760451949… 30    4.523… 23    4.40… 7    
4 Afghanistan <NA>       Some  0.337138465643189… 393   0.416… 230   0.25… 163  
5 Afghanistan <NA>       Not … 0.309526616406561… 287   0.347… 170   0.27… 117  
6 Afghanistan <NA>       Noth… 0.292750581876620… 257   0.188… 75    0.39… 182  
# ℹ 36 more variables: `Age Cohort` <chr>, ...11 <chr>, ...12 <chr>,
#   ...13 <chr>, ...14 <chr>, ...15 <chr>,
#   `General Educational Background` <chr>, ...17 <chr>, ...18 <chr>,
#   ...19 <chr>, ...20 <chr>, ...21 <chr>, `Area Type` <chr>, ...23 <chr>,
#   ...24 <chr>, ...25 <chr>, `Identifies with Specific Religion` <chr>,
#   ...27 <chr>, ...28 <chr>, ...29 <chr>, `Per Capita Income Quintile` <chr>,
#   ...31 <chr>, ...32 <chr>, ...33 <chr>, ...34 <chr>, ...35 <chr>, …

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national <- data[c('...1', '...2', '...3', 'National results','...5')]
colnames(national) <- c("Country", "Question", "Response", "National_Results_Col_Per", "National_Results_Count")

national <- national[!(national$Country %in% c(NA, 'Country')),]
#drop title row since renamed cols alr

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national$QNumber <- str_split_i(national$Question, " ", 1)
national

# A tibble: 35,845 × 6
   Country     Question   Response National_Results_Col…¹ National_Results_Count
   <chr>       <chr>      <chr>    <chr>                  <chr>                 
 1 Afghanistan "Q1 How m… A lot    2.4561117604519497E-2  30                    
 2 Afghanistan  <NA>      Some     0.33713846564318944    393                   
 3 Afghanistan  <NA>      Not much 0.30952661640656171    287                   
 4 Afghanistan  <NA>      Nothing… 0.29275058187662067    257                   
 5 Afghanistan  <NA>      (DK)     3.6023218469106791E-2  33                    
 6 Afghanistan  <NA>      Total    1                      1000                  
 7 Afghanistan "Q2 How m… All of … 1.9034573690898671E-2  21                    
 8 Afghanistan  <NA>      Some of… 0.38102335826789896    428                   
 9 Afghanistan  <NA>      Not muc… 0.30997405231133479    305                   
10 Afghanistan  <NA>      None of… 0.24654838910650081    208                   
# ℹ 35,835 more rows
# ℹ abbreviated name: ¹National_Results_Col_Per
# ℹ 1 more variable: QNumber <chr>

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asia <- c('Afghanistan', 'Bangladesh', 'Cambodia', 'China', 'India', 'Indonesia', 'Japan', 'Laos', 'Malaysia', 'Mongolia', 'Myanmar', 'Nepal', 'Pakistan', 'Philippines', 'Singapore', 'South Korea', 'Sri Lanka', 'Taiwan', 'Thailand', 'Vietnam'
)

mena <- c('Algeria', 'Egypt', 'Iran', 'Iraq', 'Israel', 'Jordan', 'Kuwait', 'Lebanon','Libya', 'Morocco', 'Palestine', 'Saudi Arabia', 'Tunisia', 'United Arab Emirates', 'Yemen'
)

americas <- c('Argentina', 'Bolivia', 'Brazil', 'Canada', 'Chile', 'Colombia', 'Costa Rica', 'Dominican Republic', 'Ecuador', 'El Salvador', 'Guatemala', "Haiti", 'Honduras','Mexico', 'Nicaragua', 'Panama', 'Paraguay', 'Peru', 'United States', 'Uruguay', 'Venezuela'
)

sub_sahara <- c('Benin', 'Botswana', 'Burkina Faso', 'Burundi', 'Cameroon', 'Congo, Rep.','Comoros', 'Chad','Eswatini','Ethiopia', 'Gabon', 'Ghana', 'Guinea', 'Ivory Coast', 'Kenya', 'Liberia', 'Madagascar', 'Malawi', 'Mali', 'Mauritania', 'Mauritius','Mozambique', 'Namibia', 'Niger', 'Nigeria', 'Rwanda', 'Senegal','Sierra Leone', 'South Africa', 'Tanzania', 'The Gambia', 'Togo','Uganda', 'Zambia', 'Zimbabwe'
)

europe <- c('Albania', 'Austria', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria','Croatia', 'Cyprus', 'Czech Republic', 'Denmark', 'Finland', 'France','Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland', 'Italy', 'Kosovo','Luxembourg', 'Macedonia', 'Malta', 'Netherlands', 'Norway', "Poland", 'Northern Cyprus', 'Montenegro', 'Portugal', 'Romania', 'Serbia', 'Slovakia', 'Slovenia', 'Spain', 'Sweden', 'Switzerland', 'Turkey', 'United Kingdom'
)

former_soviet <- c('Armenia', 'Azerbaijan', 'Belarus', 'Estonia', 'Georgia', 'Kazakhstan', 'Kyrgyzstan', 'Latvia', 'Lithuania', 'Moldova', 'Russia', 'Tajikistan', 'Ukraine', 'Uzbekistan', 'Turkmenistan'
)

oceania <- c('Australia', 'New Zealand')

Show code

national1 <- national |>
  fill(QNumber, .direction = "down") |>
  #Source for .direction: https://tidyr.tidyverse.org/reference/fill.html
  filter(QNumber == "Q25") |>
  filter(Response == "Strongly agree" | Response == "Somewhat agree") |> group_by(Country) |>
  summarize(Agree_Percent = sum(as.numeric(National_Results_Col_Per)), .groups = "drop") |> 
  mutate(Region = case_when(
    
    Country %in% asia ~ "Asia",
    Country %in% mena ~ "Middle East and North Africa",
    Country %in% americas ~ "Americas",
    Country %in% sub_sahara ~ "Sub-Saharan Africa",
    Country %in% europe ~ "Europe",

    Country %in% former_soviet ~ "Former Soviet Union",
    
    Country %in% oceania ~ "Oceania",
    
    TRUE ~ "Other" #used to check, there are none that fall in this category
  ))
  #summarize function : https://stackoverflow.com/questions/62891736/sum-sub-groups-with-dplyr

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outliers <- national1|>
  group_by(Region)|>
  mutate(
    Q1 = quantile(Agree_Percent, 0.25),  Q3 = quantile(Agree_Percent, 0.75),
    IQR = Q3 - Q1,
    is_outlier = Agree_Percent < (Q1 - 1.5 * IQR) | Agree_Percent > (Q3 + 1.5 * IQR)
  ) |>
  filter(is_outlier)

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#| layout-ncol: 2
knitr::include_graphics("images/Capture3.jpg")

Show code

#https://stackoverflow.com/questions/77144719/how-to-embed-an-image-into-a-quarto-document-in-r


ggplot(national1, aes(x = Region,y=Agree_Percent,fill = Region))+
  geom_boxplot(outlier.shape= NA,alpha = 0.7) +  
  geom_point(data = outliers, aes(x =Region, y= Agree_Percent), shape =16, size =3, color = "black") +
  geom_text(
    data =outliers,
    aes(label= Country), vjust =0.25, hjust=-0.25,  size = 4, family="serif"
  )+ scale_y_continuous(labels = percent_format(accuracy = 1)) +
  labs(
    title = "The Percentage of People Who Believe Vaccines are Safe by Global Region",
    x= "Region",y= NULL,
  ) +theme_minimal(base_family = "serif")+
  
  theme(axis.text.x = element_text(angle = 20, hjust = 0.75), axis.text = element_text(size = 10,color = "black"), legend.position = "none"
        )

Part Two: Broad Visualization Improvement

Second Data Visualization Improvement

Select a data visualization in the report that you think could be improved. Be sure to cite both the page number and figure title. Do your best to tell the story of this graph in words. That is, what is this graph telling you? What do you think the authors meant to convey with it?

Chart 5.7: Map of proportions of people reporting vaccinating their children on page 121. This graph is telling us what percentage of the people in a particular country say their children have been vaccinated. It compares between countries, highlighting where this percentage is particularly high or low.

Show code

knitr::include_graphics("images/Capture.jpg")

List the variables that appear to be displayed in this visualization.

Percentage of people who answered ‘yes’ to their children having received vaccines and Country,

Now that you’re versed in the grammar of graphics (ggplot), list the aesthetics used and which variables are specified for each.

The map shows the variable country, the color is filled by the percentage of people who answered ‘yes’.

What type of graph would you call this?

I would call this a chloropleth map!

Source: https://support.esri.com/en-us/gis-dictionary/choropleth-map#:~:text=From%20the%20Greek%20terms%20choro,combining%20different%20sets%20of%20symbols.

List all of the problems or things you would improve about this graph.

I would make the color scaling more distinct and more apparent as it is difficult to visibly see the percentage or people who said their children had been vaccinated.
I might add in global region to see if that shows any distinctions.
I would make the countries that were not surveyed a more distinct color from the background color as it is hard to notice island nations and the like.

Improve the visualization above by either re-creating it with the issues you identified fixed OR by creating a new visualization that you believe tells the same story better.

Show code

national2 <- national |>
  fill(QNumber, .direction = "down") |>
  #Source for .direction: https://tidyr.tidyverse.org/reference/fill.html
  filter(QNumber == "Q28")|> filter(Response == "Yes") |> mutate(Region = case_when(
    
    Country %in% asia ~ "Asia",
    Country %in% mena ~ "Middle East and North Africa",
    Country %in% americas ~ "Americas",
     Country %in% sub_sahara ~ "Sub-Saharan Africa",
    Country %in% europe ~ "Europe",
    
    Country %in% former_soviet ~ "Former Soviet Union",
    
    Country %in% oceania ~ "Oceania",
    
    TRUE ~ "Other" #used to check, there are none that fall in this category
  ))
head(national2)

# A tibble: 6 × 7
  Country     Question    Response National_Results_Col…¹ National_Results_Count
  <chr>       <chr>       <chr>    <chr>                  <chr>                 
1 Afghanistan Q28 (If re… Yes      0.95331555488694564    632                   
2 Albania     Q28 (If re… Yes      0.95328945962213618    615                   
3 Algeria     Q28 (If re… Yes      0.97813253179775439    451                   
4 Argentina   Q28 (If re… Yes      0.97761035249142625    703                   
5 Armenia     Q28 (If re… Yes      0.89102412357204641    615                   
6 Australia   Q28 (If re… Yes      0.95963873905667174    713                   
# ℹ abbreviated name: ¹National_Results_Col_Per
# ℹ 2 more variables: QNumber <chr>, Region <chr>

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world <- ne_countries(scale = "medium", returnclass = "sf")
world$Country <- tolower(world$name_long)
national2$Country <- tolower(national2$Country)

world_data <- left_join(world, national2, by = c("Country" = "Country"))
world_data$National_Results_Col_Per <- as.numeric(world_data$National_Results_Col_Per)

#world_data$National_Results_Col_Per[is.na(world_data$National_Results_Col_Per )] <- "Countries not surveyed"

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#colorpalette
pal <- colorBin(
  palette =c("#ffffd4", "#fec44f", "#fe9929", "#fc4e2a", "#b10026"),
  #https://loading.io/color/feature/YlOrRd-8
  domain = world_data$National_Results_Col_Per,
  bins =c(0, 0.70, 0.80, 0.90, 0.95, 1.00), na.color ="#d3d3d3"  
)

#plot
leaflet(world_data) |> addTiles() |>
  addPolygons(
    fillColor = ~pal(National_Results_Col_Per), weight = 1,
    color = "white", fillOpacity = 0.8,
    label = ~paste0(Country, ": ", National_Results_Col_Per, "%"),
    highlight = highlightOptions(
      weight = 2, color = "#666",
      fillOpacity = 0.9, bringToFront = TRUE
    )
    #Source: https://r-charts.com/spatial/interactive-maps-leaflet/
  ) |>
  
  addLegend(
    pal = pal, values = ~National_Results_Col_Per,
    title = "Percentage of people who say  <br> their child has been vaccinated", position = "bottomright"
    #Source line break: https://www.reddit.com/r/Rlanguage/comments/6bsji1/add_line_break_to_leaflet_pop_up/
  )

Third Data Visualization Improvement

Select a data visualization in the report that you think could be improved. Be sure to cite both the page number and figure title. Do your best to tell the story of this graph in words. That is, what is this graph telling you? What do you think the authors meant to convey with it?

Show code

knitr::include_graphics("images/Capture2.jpg")

List the variables that appear to be displayed in this visualization.

The percentages for all of the following columns: Men, Women, 15-29, 30-49,50+, Elementary education or less, Secondary education, Post-secondary education, Rural/small town, Big city/suburb, as well as separate created onee like Parents and Non-Parents

Now that you’re versed in the grammar of graphics (ggplot), list the aesthetics used and which variables are specified for each.

The x axis is the demographic group while the y-axis is the percentage of the group that believes vaccines are unsafe!

What type of graph would you call this?

A bar plot!

List all of the problems or things you would improve about this graph.

I would facet this better and change the background color as the yellow almost blends in with the lighter blue background.

Improve the visualization above by either re-creating it with the issues you identified fixed OR by creating a new visualization that you believe tells the same story better.

Show code

national3 <- data[c('...1', '...2','...3','National results','...5',
                    'Gender','...7', '...8', '...9',
                     'Age Cohort','...11', '...12',
                    '...13', '...14', '...15', 'General Educational Background', 
                  '...17','...18', '...19', 
                    '...20','...21','Area Type', 
                  '...23', '...24','...25')]

colnames(national3)<- c("Country", "Question", "Response",
                         "National_Results_Col_Per", "National_Results_Count",
                         "Men", "Man_num", "Women", "Woman_num",
                         "15-29", "15_29_Num","30-49",
                 "30_49_Num", "50+", "Above_50_Num",
                         "Elementary education or less", "elementary_num",
                         "Secondary education","secondary_num", 
                         "Post-secondary education","college_num", 
                         "Rural/small town","rural_num", 
                         "Big city/suburb","suburb_num")

national3 <- national3[!(national3$Country %in% c(NA, 'Country')),]
national3$QNumber <- str_split_i(national3$Question, " ", 1)

national3 <- national3 |> 
  filter(Country == "France")|> fill(QNumber, .direction = "down") |>
  filter(QNumber == "Q25")|> filter(Response %in% c("Strongly disagree","Somewhat disagree")) |>
  mutate(across(c(National_Results_Col_Per, Men, Women, `15-29`, `30-49`,`50+`, 
                  `Elementary education or less`, `Secondary education`, `Post-secondary education`,
                  `Rural/small town`, `Big city/suburb`),as.numeric)) |>
  
  summarize(across(c(National_Results_Col_Per,Men, Women, `15-29`, `30-49`,`50+`, 
                  `Elementary education or less`, `Secondary education`, `Post-secondary education`,
                  `Rural/small town`, `Big city/suburb`), sum, na.rm = TRUE), .groups = "drop")|>
  mutate(Country = "France")

Warning: There was 1 warning in `summarize()`.
ℹ In argument: `across(...)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))

Show code

national3

# A tibble: 1 × 12
  National_Results_Col_Per   Men Women `15-29` `30-49` `50+`
                     <dbl> <dbl> <dbl>   <dbl>   <dbl> <dbl>
1                    0.333 0.328 0.338   0.373   0.335 0.316
# ℹ 6 more variables: `Elementary education or less` <dbl>,
#   `Secondary education` <dbl>, `Post-secondary education` <dbl>,
#   `Rural/small town` <dbl>, `Big city/suburb` <dbl>, Country <chr>

Show code

data2<-read_excel("C:\\Users\\Geetha\\Downloads\\wgm2018-dataset-crosstabs-all-countries.xlsx", sheet = "Full dataset")
head(data2)

# A tibble: 6 × 60
    WP5   wgt  PROJWT FIELD_DATE          YEAR_CALENDAR    Q1    Q2    Q3    Q4
  <dbl> <dbl>   <dbl> <dttm>                      <dbl> <dbl> <dbl> <dbl> <dbl>
1     1 0.653 171770. 2018-01-08 00:00:00          2018     3     2     1     2
2     1 0.696 183053. 2018-01-08 00:00:00          2018     2     2     1     2
3     1 0.524 137829. 2018-01-08 00:00:00          2018     2     2     1    98
4     1 0.764 201139. 2018-01-08 00:00:00          2018     2     1     1     2
5     1 3.33  875646. 2018-01-08 00:00:00          2018     2     1     1     2
6     1 0.371  97672. 2018-01-08 00:00:00          2018     1     1     1     2
# ℹ 51 more variables: Q5A <dbl>, Q5B <dbl>, Q5C <dbl>, Q6 <dbl>, Q7 <dbl>,
#   Q8 <dbl>, Q9 <dbl>, Q10A <dbl>, Q10B <dbl>, Q11A <dbl>, Q11B <dbl>,
#   Q11C <dbl>, Q11D <dbl>, Q11E <dbl>, Q11F <dbl>, Q11G <dbl>, Q12 <dbl>,
#   Q13 <dbl>, Q14A <dbl>, Q14B <dbl>, Q15A <dbl>, Q15B <dbl>, Q16 <dbl>,
#   Q17 <dbl>, Q18 <dbl>, Q19 <dbl>, Q20 <dbl>, Q21 <dbl>, Q22 <dbl>,
#   Q23 <dbl>, Q24 <dbl>, Q25 <dbl>, Q26 <dbl>, Q27 <dbl>, Q28 <dbl>, D1 <dbl>,
#   Q29 <dbl>, Q30 <dbl>, WGM_Index <dbl>, WGM_Indexr <dbl>, …

Show code

# France's WP5 is 13
france<- data2|> filter( WP5 == 13) |> select(WP5, Q25, Q27)|>group_by(Q27, Q25) |>
  summarise(count = n(), .groups = "drop_last") |>
  mutate(percentage = round(count / sum(count), 6)) |> ungroup() |>
  filter(Q25 %in% c(4, 5)) |> group_by(Q27) |>
  summarise( total_count = sum(count),
             total_percentage = sum(percentage)) |> pivot_wider(
    names_from = Q27,
    values_from = c(total_count, total_percentage),
    names_glue = "Q27_{Q27}_{.value}") |> select(Q27_1_total_percentage, Q27_2_total_percentage) |>mutate(Country = "France")


colnames(france) <- c("Parents", "Non-Parents", "Country")

france

# A tibble: 1 × 3
  Parents `Non-Parents` Country
    <dbl>         <dbl> <chr>  
1   0.314         0.299 France

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france_all<- merge(national3, france, by = "Country")

france_all

  Country National_Results_Col_Per       Men     Women     15-29    30-49
1  France                0.3329385 0.3277665 0.3375892 0.3727464 0.335249
        50+ Elementary education or less Secondary education
1 0.3158295                     0.354163           0.3378381
  Post-secondary education Rural/small town Big city/suburb  Parents
1                0.3017101        0.3517994       0.3073689 0.313665
  Non-Parents
1    0.299145

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france_long <- france_all |>
  pivot_longer(
    cols = -c(Country, National_Results_Col_Per),
    names_to = "Category",
    values_to = "Percentage"
  )|>mutate(Group = case_when(
   Category %in% c("Men", "Women") ~ "Gender",
    Category %in% c("15-29", "30-49", "50+") ~ "Age",
     Category %in% c("Elementary education or less", "Secondary education", "Post-secondary education") ~ "Education",
    Category %in% c("Rural/small town", "Big city/suburb") ~ "Region",
    
    Category %in% c("Parents", "Non-Parents") ~ "Parenthood",
    TRUE ~ "Other"
  ))
france_long

# A tibble: 12 × 5
   Country National_Results_Col_Per Category                    Percentage Group
   <chr>                      <dbl> <chr>                            <dbl> <chr>
 1 France                     0.333 Men                              0.328 Gend…
 2 France                     0.333 Women                            0.338 Gend…
 3 France                     0.333 15-29                            0.373 Age  
 4 France                     0.333 30-49                            0.335 Age  
 5 France                     0.333 50+                              0.316 Age  
 6 France                     0.333 Elementary education or le…      0.354 Educ…
 7 France                     0.333 Secondary education              0.338 Educ…
 8 France                     0.333 Post-secondary education         0.302 Educ…
 9 France                     0.333 Rural/small town                 0.352 Regi…
10 France                     0.333 Big city/suburb                  0.307 Regi…
11 France                     0.333 Parents                          0.314 Pare…
12 France                     0.333 Non-Parents                      0.299 Pare…

Show code

ggplot(france_long, aes(x = reorder(Category, Percentage), y = Percentage, fill = Category)) + geom_bar(stat = "identity", width = 0.7) + coord_flip() +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  facet_wrap(~ Group, scales = "free_y",ncol = 1) +
  labs(title = "Proportion of people in France who believe vaccines \nare unsafe by Demographic Group",
       x = "",
       y = "") +
  theme_minimal() +
  theme(legend.position = "none")